Source identification of personal exposure to fine particulate matter (PM2.5) among adult residents of Hong Kong

Journal Pre-proof Source identification of personal exposure to fine particulate matter (PM2.5) among adult residents of Hong Kong Xiao-Cui Chen, Tony J. Ward, Jun-Ji Cao, Shun-Cheng Lee, Ngar-Cheung Lau, Steve HL. Yim, Kin-Fai Ho PII:

S1352-2310(19)30638-7

DOI:

https://doi.org/10.1016/j.atmosenv.2019.116999

Reference:

AEA 116999

To appear in:

Atmospheric Environment

Received Date: 6 May 2019 Revised Date:

12 August 2019

Accepted Date: 20 September 2019

Please cite this article as: Chen, X.-C., Ward, T.J., Cao, J.-J., Lee, S.-C., Lau, N.-C., Yim, S.H., Ho, K.F., Source identification of personal exposure to fine particulate matter (PM2.5) among adult residents of Hong Kong, Atmospheric Environment (2019), doi: https://doi.org/10.1016/j.atmosenv.2019.116999. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Graphic abstract:

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Source identification of personal exposure to fine particulate matter (PM2.5)

3

among adult residents of Hong Kong

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Xiao-Cui Chena, Tony J. Wardb, Jun-Ji Caoc,d, Shun-Cheng Leee, Ngar-Cheung Laua,f, Steve HL

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Yima,f, Kin-Fai Hoa,c,g*,h

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a

Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong,

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Hong Kong, China

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b

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c

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Sciences, Xi’an, China

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d

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e

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Kowloon, Hong Kong, China

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f

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Hong Kong

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g

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Kong, Hong Kong, China

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h

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The Chinese University of Hong Kong, Shenzhen, China

School of Public and Community Health Sciences, University of Montana, Missoula, MT, USA

Key Laboratory of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of

Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an, China

Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University,

Department of Geography and Resource Management, The Chinese University of Hong Kong,

The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong

Shenzhen Municipal Key Laboratory for Health Risk Analysis, Shenzhen Research Institute,

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-----------------------------------------------------------∗

Correspondence: Dr. Kin-Fai Ho, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China. Tel.: 852-2252-8763. Fax: 852-2606-3500. E-mail: [email protected]

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Abstract

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Epidemiological studies provide evidence of the harmful effects of source-specific fine

34

particulate matter (PM2.5) on human health. Studies regarding relative contributions of multiple

35

sources to personal exposure are limited and inconsistent. Personal exposure monitoring for

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PM2.5 was conducted in 48 adult subjects (ages 18‒63 years) in Hong Kong between June 2014

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and March 2015. We identified seven sources of personal PM2.5 exposure using Positive Matrix

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Factorization (PMF). These sources included regional pollution (associated with coal combustion

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and biomass burning), secondary sulfate, tailpipe exhaust, secondary nitrate, crustal/road dust,

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and shipping emission sources. For personal PM2.5 exposure, one additional source related to an

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individual’s activity was found: non-tailpipe pollution (characterized by Fe, Mn, Cr, Cu, Sr). We

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also applied principal component analysis (PCA) for PM2.5 source identification. The results

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revealed similar factor/component profiles using PMF and PCA, with some discrepancies in the

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number of factors. PCA/absolute principal component scores (PCA/APCs) coupled with a linear

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mixed-effects model (LMM) was applied to the same dataset for source apportionment, adjusting

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for temperature and relative humidity. Furthermore, stratified PCA/APCs-LMM models were

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applied to estimate season- and group-specific source contributions of personal PM2.5 exposure.

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A mixed source contributions of secondary sulfate, secondary nitrate, and regional pollution

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were shown (35.1‒43.6%), with no seasonal or subject group differences (p > 0.05). Shipping

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emissions were ubiquitous, contributing 6.3‒8.8% of personal PM2.5 exposure for all subjects.

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Tailpipe exhaust and traffic-related particles varied by season (p < 0.01) and subject group (p <

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0.05). Caution should be taken when using source-specific PM2.5 as proxies for the

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corresponding personal exposures in epidemiological studies.

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Keywords: Personal fine particles exposure; Ambient air pollution; Positive Matrix

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Factorization; Principal component analysis/Absolute Principal Component Scores; Mixed-

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effects model

57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

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1. Introduction

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Epidemiological studies have linked ambient PM2.5 (particles with aerodynamic diameter <

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2.5 µm) pollution with adverse health outcomes globally (Franklin et al., 2006; Kim et al., 2015;

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Peng et al., 2009; Wong et al., 2015). These studies used stationary ambient concentrations as a

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proxy for personal exposures. People spend the majority (> 85%) of their daily time indoors

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(Chau et al., 2002; Chen et al., 2018). The World Health Organization reported that indoor

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(household) air pollution has equal or more significant health risks compared with ambient air

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pollution (WHO, 2016). Concern over the impacts of exposure measurement error on health risk

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estimates in time-series studies created a need to differentiate between personal exposure of

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ambient- and non-ambient components (Rhomberg et al., 2011; Zeger et al., 2000).

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PM2.5 is a complex mixture with chemical constituents, derived from various sources with

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different toxicities (Krall et al., 2017; Lakey et al., 2016; Pun et al., 2014; Stanek et al., 2011).

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Evidence indicated that aside from particles of ambient origin, indoor sources (e.g., cooking,

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cleaning, exposure to tobacco smoke) and source related to personal activities strongly affect

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total personal PM2.5 exposure (Meng et al., 2009; Shang et al., 2019; Wan et al., 2011). Tailpipe

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exhaust, tire/brake wear, and road dust exhibit a spatially heterogeneous distribution (Krall et al.,

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2018); personal exposure to traffic-related pollutants varied across individuals and (or)

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subpopulations (Baccarelli et al., 2014; Chen et al., 2018). The findings revealed that mixed

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source-specific PM2.5 contribute to elevated personal exposures (Chen et al., 2017b; Kim et al.,

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2005; Koistinen et al., 2004; Larson et al., 2004; Shang et al., 2019). Additionally, these studies

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pointed out that source identification and apportionment of personal PM2.5 exposure warrant

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further investigation.

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Past studies have shown that human exposure to PM2.5 components, including organic carbon

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(OC), elemental carbon (EC), sulfate (SO42-), nitrate (NO3-), ammonium (NH4+) and elements

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(e.g., Na, Si, K, Ca, Cu, Zn, V) were associated with increased cardiovascular and respiratory

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hospitalizations and mortality (Achilleos et al., 2017; Pun et al., 2014; Tian et al., 2013). Source

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apportionment techniques, such as Positive Matrix Factorization (PMF) and Principal

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Component Analysis (PCA) with Absolute Principal Component Scores (APCs) have been used

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with ambient and (or) indoor datasets to estimate the contribution of specific sources to PM2.5.

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Other studies have conducted regression analyses to differentiate the contribution of indoor and

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outdoor sources to particles in residential houses (Hassanvand et al., 2014; Xu et al., 2014). A

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few studies also applied these methods to determine the potential source contributing to personal

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exposure (Hopke et al., 2003; Koistinen et al., 2004; Larson et al., 2004; Minguillon et al., 2012).

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Most of these exposure studies were conducted in North America and European cities

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(Johannesson et al., 2007; Weisel et al., 2004; Williams et al., 2000).

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apportionment studies using personal monitoring data, Kim et al. (2005) applied the mixed-

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effects models to estimate the relative contribution of primary sources of personal PM2.5

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exposures. Additional studies have characterized the sources of personal PM2.5 exposure for

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young students in Chinese megacities (e.g., Tianjin, Guangzhou) (Chen et al., 2017b; Zhang et

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al., 2015), with few studies focused on the seasonal or occupational differences (Shang et al.,

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2019).

Regarding source

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We conducted repeated personal measurements in adult subjects of Hong Kong. The results

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regarding the determinants of personal exposure to PM2.5 mass and its components were reported

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elsewhere (Chen et al., 2018). The objectives of the current study were to 1) conduct personal

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PM2.5 source apportionment using PMF and PCA, and 2) characterize the seasonal and subject-

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group differences regarding source contribution of personal PM2.5 exposure using PCA/APCs

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along with a linear mixed-effects model (LMM). This study provides an opportunity to leverage

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the advantage of different modeling techniques in source apportionment of personal PM2.5

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exposure. The findings also provide comprehensive information for epidemiologists and

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regulatory agencies regarding mitigation strategies for particulate pollution in Hong Kong and

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other cities with elevated air pollution exposures to their residents.

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2. Materials and methods

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2.1. Study subjects and protocol

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Forty-eight adults (ages 18‒63) participated in the personal monitoring campaign. All subjects

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(including 12 office workers, 16 college students, 12 housewives, and 8 non-office workers)

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were non-smokers, not exposed to environmental tobacco smoke in indoor microenvironments

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(e.g., home, school, office, or other indoors). Forty-two and 41 participants participated in

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summer (between July and October 2014) and winter campaign (between December 2014 and

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March 2015), respectively. The Joint Chinese University of Hong Kong-New Territories East

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Cluster Clinical Research Ethics Committee approved this study before starting. Each subject

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signed the informed consent before participation.

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2.2. Personal monitoring

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Twenty-four-hour (24-hr, 00:00–24:00 local time) personal PM2.5 samples were collected using a

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Personal Environmental Monitor (PEMs, Model 200, MSP Corp., Shoreview, MN, USA)

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together with a Leland Legacy sampling pump (SKC Inc., Eighty-Four, PA, USA) operated at a

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flow rate of 10 L/min. Two PEMs loaded with one Teflon membrane and one quartz fiber filter

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(37 mm, 2 µm pore size, Pall Corporation, MI, USA), respectively, were carried by each subject. 6

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Personal exposure monitoring from each subject was conducted in a two-day sampling event

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during the summer and winter session, respectively. A total of 161 personal samples were

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collected throughout the study period. Study subjects were required to wear the PEMs near the

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breathing zone during each 24-hr sampling session, except for sleeping and sitting, in which case

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the samplers were located in proximity to the subjects (< 1 meter). Subjects were encouraged to

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maintain regular daily activity patterns. A questionnaire regarding participants’ personal

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information (i.e., age, sex, occupation, and residential characteristics) was acquired from each

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subject before participating in this study. Participants were also required to complete a 24-hr

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time-activity diary during each sampling session (Tables S1–S2). Fig. 1 shows the subjects’

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residential location in the study area in Hong Kong. Further detail on quality assurance and

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quality control during personal monitoring and sample collection has been reported previously

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(Chen et al., 2017a).

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2.3. Description of ambient data

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Ambient PM2.5 samples were acquired every sixth day at seven fixed monitoring stations in

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Hong Kong (including roadside Air Quality Monitoring Site [AQMS] at Mong Kok; the urban

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AQMSs at Central/Western [CW], Tsuen Wan [TW] and Kwai Chung [KC]; the new town

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AQMSs at Tung Chung [TC] and Yuen Long [YL]; the suburban AQMS at Clear Water Bay

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[WB]) (Fig. 1). Daily meteorological data including temperature (T), relative humidity (RH),

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rainfall (R), atmospheric pressure (P), wind speed (WS), and wind direction (WD) retrieved from

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the Hong Kong Observatory (HKO, http://www.weather.gov.hk/contente.htm) were assembled.

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The location of the nearest weather stations for each AQMS is shown in Fig. 1.

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Fig. S1 depicts the corresponding distances between seven central monitoring stations and

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subjects’ residences, with an average distance of ~14.9 km (range 0.1–34.5 km). In this

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investigation, ambient data (269 sampling days, including PM2.5 mass and constituents) for the

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overlapping sampling period of July–October 2014 and December 2014–March 2015 were

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analyzed (Fig. S2). Additional details about ambient data can be found in our recent publication

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(Chen et al., 2019).

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2.4. Filter analyses

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Triplicate filter weights (± 3 µg) were determined on Teflon filters using an electronic

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microbalance (readability of 1 µg, Sartorius AG, Model ME 5-0CE, Goettingen, Germany) in a

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temperature (20‒25 °C) and relativity humidity (35 ± 5%) controlled weighing room at The

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Hong Kong Polytechnic University, Hong Kong. Carbonaceous aerosols (including organic

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carbon [OC] and elemental carbon [EC]) were analysed on quartz filters by the thermal/optical

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reflectance (TOR) method following the IMPROVE_A protocol using a DRI Model 2001

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Thermal/Optical Carbon Analyzer (Atmoslytic Inc., Calabasas, CA, USA) (Chow et al., 2011).

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Method

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0.04 µg/m3, respectively. Additionally, water-soluble anions (including chloride [Cl-], nitrate

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[NO3-], sulfate [SO42-], and oxalate [C2O42-]) and cations (including sodium [Na+], ammonium

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[NH4+], potassium [K+], magnesium [Mg2+], and calcium [Ca2+]) were quantified using an Ion

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Chromatograph (Dionex ICS-3000, USA) on quartz filters (Ho et al., 2014). MDLs of ions were

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within the range of 0.01‒0.23 µg/m3. Blank samples were analyzed along with filter samples.

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Filed and lab blanks were applied to correct for the residual amount of the water-soluble ions on

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quartz filters. In this study, concentrations of the analyzed carbonaceous aerosols and ionic

detection

limits

(MDLs)

of

8

OC

and

EC

were

0.28

and

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species were corrected by subtracting the blank values.

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Teflon filter samples were sent to Desert Research Institute laboratories (DRI, Reno, NV,

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USA) in a temperature-controlled cooler (< 4˚C), where elemental analyses by Energy

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Dispersive X-Ray Fluorescence (ED-XRF, Epsilon 5, PANalytical Company, Netherlands) was

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conducted (Watson et al., 1999). A total of 24 elements (i.e., sodium [Na], magnesium [Mg],

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aluminium [Al], silicon [Si], sulfur [S], chlorine [Cl], potassium [K], calcium [Ca], titanium [Ti],

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vanadium [V], chromium [Cr], manganese [Mn], iron [Fe], nickel [Ni], copper [Cu], zinc [Zn],

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arsenic [As], bromine [Br], Rubidium [Rb], Strontium [Sr], Zirconium [Zr], Molybdenum [Mo],

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Tin [Sn], and lead [Pb]) were quantified (Chow and Watson, 2012). MDLs of analyzed elements

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were within the range of 0.5–33 ng/m3 (Table S3). The analyzed 24 elements were detectable

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(i.e., > MDLs) for > 65% of the samples except Se, Mo and Sn (31‒63% were detectable).

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Quality assurance (QA) and quality control (QC) protocols were followed when conducting

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personal monitoring (see Supporting Information). Additional details about personal PM2.5

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gravimetric and chemical analyses can be found in our recent publication (Chen et al., 2018).

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2.5. Source apportionment

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We employed the U.S. Environmental Protection Agency’s Positive Matrix Factorization (EPA

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PMF 5.0 software) receptor model to estimate source contributions to personal PM2.5 exposure in

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adult subjects living in Hong Kong. Details about PMF modeling are described in (Hopke et al.,

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2003). PM2.5 component concentrations, together with the uncertainties, were utilized in the PMF

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analysis. Missing data were replaced by the median concentrations of components along with

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four times the uncertainty; measured concentrations below MDLs were assigned as half of the

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MDLs value along with uncertainty of 5/6 MDLs (Reff et al., 2007). When there were redundant

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tracers, only one of them would be included. For example, Na and Ca were kept to prevent

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double-counting in PMF analysis; for Cl, Mg, S, and K, the ions data (i.e., Cl-, SO42-, Mg2+, K+)

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were retained. We set a 10% extra modeling uncertainty for PMF analysis. We applied error

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estimation (EE) diagnostics to determine the optimal number of source factors. In the present

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study, seven source factors (Fpeak = -0.5) were selected in the PMF analysis, which was optimal

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and balancing the statistical robustness and physical interpretability. Detailed information related

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to EE diagnostics can be found in Brown et al. (2015).

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We applied PCA for source identification of ambient and personal PM2.5. Varimax normalized

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rotation was performed to maximize or minimize the factor loadings of principal components

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(PCs). The PCs with an eigenvalue greater than one entered in the source identification analysis.

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The absolute principal component scores (APCs) for each component was calculated by

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subtracting the absolute zero factor from the corresponding factor scores in the standard PCA

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analysis (Guo et al., 2004; Thurston and Spengler, 1985). We applied PCA/APCs incorporated

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with linear mixed-effects models to quantify source contributions of personal exposure to PM2.5.

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The mixed-effects model was constructed with personal PM2.5 exposure as the dependent

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variable and APCs as the independent variables. The APCs were regressed against personal

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PM2.5 exposure to estimate source contributions. The contribution of each source was calculated

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by multiplying the regression coefficients from the mixed-effects model by their respective

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concentrations and dividing by the fitted values. The PCA/APCs-LMM expressed as follows:

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=

+∑

+

+

Eq. 1

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where Yij is the exposure level on the jth day for ith subject, β0 is the intercept of the regression

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for the exposures, βm represents the regression coefficient for the APCsm; bi represents the

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random effect for ith subject, and εij refers to the random effects of exposures on jth day for ith 10

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subject. The random effects of bi and εij are presumed to be mutually independent with means of

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zero and between-subject (σ2b) and within-subject (σ2w) variance (Eq.1). The mixed-effects

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regression analysis was stratified by season to account for the heterogeneity of the source

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contributions of personal PM2.5 exposure. The variation between subjects with different

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occupations merit further discussion. Thus, we divided all subjects into two groups (group A,

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including office worker and student; group B, including housewife and non-office worker) to

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account for the heterogeneity of source contributions to personal PM2.5 exposure. We used

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backward stepwise regression method to eliminate non-significant (p > 0.05) variables.

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2.6. Statistical analysis

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Seasonal and occupational differences in personal exposure to PM2.5 components were evaluated

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using analysis of variance (ANOVA). We utilized the same dataset to conduct both PMF and

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PCA/APCs-LMM modeling results. Spearman’s correlation coefficient (rs) was applied to

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characterize the associations of chemical constituents in personal PM2.5. We illustrated linear

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mixed-effects regression models using lmer from the lme4 and lmerTest package in R 3.4.4 (The

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R Project for Statistical Computing, 2018: http://www.r-project.org) (Bates et al., 2014). We

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used the conditional R2 statistic (R2c) to estimate the overall predictive ability of the mixed-

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effects model, and marginal R2 (R2m) was calculated for each APCs in the mixed-effects models

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(Jaeger, 2016; Jaeger et al., 2016). A p-value < 0.05 was considered statistically significant.

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3. Results

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3.1. Characteristic of personal exposure to PM2.5 components

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The mean and standard deviation of the analyzed PM2.5 components in personal exposure are

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shown in Tables 1-2 by season and by subject group. Because of the extra trace elements

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included (e.g., Se, Rb, Sr, Zr, Mo, Sn) in this analysis, there were slight mass differences

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between the elements reported in this study and those listed in our previous publication in (Chen

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et al., 2018).

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OC and EC accounted for 24.3 ± 10.3% and 7.2 ± 5.7% of personal PM2.5, respectively, with

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no significant seasonal differences (p > 0.05). SO42- was the most abundant component,

266

accounting for 26.4‒28.9% of personal PM2.5 mass, followed by NH4+ (7.2‒24.3%) and NO3-

267

(7.0‒11.1%). All water-soluble ions exhibited significant seasonal variations (p < 0.01) with

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higher concentrations in winter and lower levels in summer except Na+ (p = 0.61). Personal Ca2+

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exposure was higher in the summer than in winter (p = 0.31). Total elements (7.0‒8.7 ng/m3)

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accounted for 22.1 ± 6.8% of personal PM2.5 mass and exhibited significant seasonal variations

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(p = 0.02). The reconstructed mass was strongly correlated with the measured personal PM2.5 (r

272

> 0.95; p < 0.01) (Chen et al., 2018).

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Higher personal OC and EC exposures were observed in housewives (p = 0.004) and non-

274

office workers (p < 0.001), respectively, compared to other subjects. Also, water-soluble ions

275

and most of the trace element (e.g., Al, S, K, As, Rb, Sr) concentrations were higher in

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housewives and non-office workers in winter than in their counterparts. Fig. 2 illustrates the

277

personal_avg to ambient_avg ratios for PM2.5 components across seasons. Fig. S2 shows the PM2.5

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component concentrations in ambient samples during the same study period. OC, Na+, secondary

279

inorganic ions (SIA, including SO42-, NO3-, NH4+), Ca, Ti, Fe, Cu, Rb, Zr, and Sn exhibited

280

higher levels in personal samples than those of ambient, with personal_avg/ambient_avg ratios

281

greater than unity in both seasons (Fig. 2).

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3.2. Source apportionment of personal PM2.5 exposure using PMF

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Fig. 3 shows the estimated PM2.5 source profiles of personal exposure, depicted as percentages of

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variables to each factor derived from the PMF model. The results were robust, with an R2 value

286

of 0.9 (p < 0.001) between experimental concentrations and fitted values. Factor 1 had high

287

loadings of V and Ni, which were the dominant species from oil combustion and considered as

288

robust tracers from shipping emissions in coastal areas (Oeder et al., 2015; Tao et al., 2017).

289

Factor 2 was explained by high loadings of secondary nitrate, chloride, and magnesium. Factor 3

290

was characterized by Al, Si, Ca, Ti, and Sr, therefore we named it crustal/road dust (Cesari et al.,

291

2016; Ho et al., 2003b). Factor 4 was identified as non-tailpipe source derived from traffic

292

pollution, characterized by high loadings of Fe, Cr, and Mn (Krall et al., 2018; Moreno et al.,

293

2018). Factor 5 was characterized by high loadings for sulfate, ammonium, and oxalate. Factor 6

294

was interpreted as tailpipe exhaust, characterized by high loadings of OC and EC as well as

295

small percentages of Ca, Rb and Zr (Schauer, 2003; Shang et al., 2019). Factor 7 was identified

296

as a composite of two sources from regional pollution that could not be further separated,

297

including biomass burning (based on the abundance of K+) (Sang et al., 2011) and coal

298

combustion from power plants and industrial facilities (based on the abundance of Cu, Zn, As, Br,

299

Pb) (Pun et al., 2015). Fig. 4 illustrates the contributions of the seven identified sources to

300

personal PM2.5 exposure. Secondary sulfate accounted for the largest fraction (25.9%) of total

301

personal PM2.5 exposure, followed by regional pollution (21.9%), tailpipe exhaust (19.0%),

302

secondary nitrate + Cl- (15.1%), shipping emissions (8.1%), crustal/road dust (5.2%) and non-

303

tailpipe particles (4.7%).

13

304

Fig. S3 illustrates the source profiles extracted from ambient samples. Personal and ambient

305

data exhibited similar source profiles, with differences being observed for traffic-related particles

306

in personal samples. Further, factor 8 had high loadings of Na (> 80%) and Cl- in ambient PM2.5

307

(Fig. S3), which was not shown in personal samples. The contribution of each source to ambient

308

PM2.5 followed the same order as those for personal PM2.5 exposure, except that marine aerosols

309

and shipping emissions accounted 15.6% and 1.1% of ambient PM2.5, respectively (Fig. S4).

310 311

3.3. Source apportionment of personal PM2.5 exposure using PCA/APCs‒LMM

312

Fig. 5 shows the Spearman’s correlation matrix for personal PM2.5 components. Table 3 lists the

313

rotated component matrix (i.e., factor loadings) from PCA in personal PM2.5. Five PCs explained

314

79.1% of the total variance in this study (Table 3). PC1 (explained 28.2% of the original data

315

variance) was characterized by high factor loadings of Al, Si, Ca, and Ti and to a lesser extent,

316

Mn, Fe, Zn, Rb, Sr, and Zr (Table 3). Further, PC1 and PC3 (characterized by high loadings of V

317

and Ni) (Table 3) closely resembled the corresponding PMF sources profile for Factor 3 and

318

Factor 1 (Fig. 3), respectively. PC2 explained 28.1% of the total variance and illustrated mixed

319

profiles with secondary nitrate (Factor 2), secondary sulfate (Factor 5), regional pollution (Factor

320

7), and tailpipe exhaust associated species (e.g., EC). PC4 included Cl- and a lower loading of

321

secondary nitrate. PC5 was characterized by Cr and Fe (accounted for 5.8% of the total variance)

322

and interpreted as non-tailpipe pollutants.

323

Table 4 displays the source contributions of personal PM2.5 exposure by using the PCA/APCs-

324

LMM model. The modeled personal PM2.5 exposure was highly correlated with the measured

325

value (R2 = 0.87; p < 0.001). On average, a combination of regional pollution, secondary nitrate

326

and sulfate, and tailpipe exhaust contributed to 61.7% of personal PM2.5 exposure. Crustal/road

14

327

dust and shipping emissions accounted for 9.3% of personal PM2.5 exposure; lower contributions

328

were shown for traffic-related particles (4.3%). PC4 (secondary nitrate + Cl-) showed

329

considerably lower contributions (3.7%) to personal PM2.5 because it mixed with other sources.

330

Component matrix from PCA in ambient PM2.5 and sources contributions of ambient PM2.5 using

331

PCA/APCs-LMM model are shown in Tables S4‒S5.

332 333

3.4. Seasonal variation and subject group-specific source contributions to personal PM2.5

334

exposure

335

Source contributions to personal PM2.5 exposure were stratified by season (n_summer = 80; n_winter

336

= 80) and by study group (n_A = 97; n_B = 63) (Table 5). Six PCs (with eigenvalues greater than

337

1) were extracted by season (Tables S6‒S7) while 5 PCs were identified across different groups

338

of subjects (Tables S8‒S9). Although varied in terms of PCs sequence, the results shared a

339

similar number of source factors and the total variance explained by the PCs were comparable

340

across seasons (79.5‒85.7%) and subject groups (75.8‒83.4%).

341

Table 5 and Fig. 6 illustrate the estimated season- and group-specific source contributions to

342

personal PM2.5 exposure using PCA/APCs‒LMM. Tailpipe exhaust (Factor 6) and traffic-related

343

particles (Factor 4) contributed 52.6% and 6.7% of personal PM2.5 exposures in summer, which

344

were significantly higher (p < 0.001) than those in winter (< 37.4% and 3.0%, respectively). In

345

contrast, the contribution of shipping emissions to personal PM2.5 exposure was significantly

346

higher in winter than in summer (8.3% vs. 7.0%; p < 0.001). The least variability was found for

347

the contributions of the combination of secondary aerosols and regional pollution (Fig. 6).

348

Shipping emissions contributed 6.2‒8.4% of total personal PM2.5 exposure across all subjects in

349

this study. Two opposite trends were shown: group A with secondary aerosols (39.6%)

15

350

contribution maximum, while tailpipe exhaust (40.2%) exhibited an increase in group B (non-

351

office workers consist of van-drivers, paper vendors).

352 353

4. Discussion

354

In the present study, we utilized the exposure data collected in adult subjects in Hong Kong to

355

estimate source contributions of personal PM2.5 exposure. Similar seasonal variations were

356

shown for both personal PM2.5 mass and most of the personal PM2.5 components, with higher

357

levels in winter and lower levels in summer. For example, significantly higher personal SIA

358

exposures were found in winter than in summer (p = 0.001). Previous literature applied

359

NO3−/SO42− ratios to estimate the relative importance of stationary versus mobile sources. The

360

NO3−/SO42− ratio < 1 indicated that particles were mainly from stationary sources, while

361

NO3−/SO42− ratio > 1 suggested that particles were mainly from mobile sources (Yao et al.,

362

2002). In this analysis, the mean NO3−/SO42- ratios in personal samples ranged from 0.31 to 0.43,

363

indicating the predominance of stationary sources (e.g., emission from sulfur-containing coal

364

combustion) over mobile sources in personal PM2.5 exposure in Hong Kong. The results were

365

comparable to those in Guangzhou, which reported NO3-/SO42- ratios of 0.3–0.4 in personal

366

PM2.5 exposure (Chen et al., 2017b). Strong Spearman’s correlation coefficients were shown

367

between ammonium with sulfate (rs = 0.94; p < 0.01) and oxalate (rs = 0.81; p < 0.01). Coal-

368

combustion related particles (i.e., S, As, Se, Pb) were significantly higher in winter than in

369

summer for personal PM2.5 exposure. We also found that sulfate was highly correlated with As

370

and Pb (rs: 0.60‒0.70; p < 0.01) (Fig. 5), indicating a coal combustion signature. Higher personal

371

SIA along with personal_avg/ambient_avg ratios > 1 for SIA may be attributed to the infiltration of

372

outdoor particles in indoor environments. Moderate to strong correlations (R2: 0.56‒0.63; p <

16

373

0.01) between time in residence and personal SIA exposure were reported in Chen et al. (2018).

374

Lower coefficient of divergence (COD) for the average sulfur (0.16) in ambient PM2.5 indicating

375

the uniform distribution of sulfur over the study area in Hong Kong (Fig. S5).

376

Comparable OC and EC mass fractions in personal PM2.5 were shown across seasons. In

377

contrast, average personal OC exposure decreased by 25.0-36.1% in office workers compared

378

with other subjects. Significant differences in personal EC exposures were shown across

379

different subject groups, with an exposure range of 1.7 ± 0.9 (e.g., office worker) to 3.0 ± 1.2

380

µg/m3 (non-office worker) (p < 0.001). Strong Spearman’s correlation coefficient was shown

381

between EC and OC (rs = 0.77, p < 0.01) (Fig. 5), indicating that they share the similar emission

382

source (e.g., vehicle exhaust). OC (rs = 0.79, p < 0.01) and EC (rs = 0.68, p < 0.01) were also

383

strongly associated with K+, a tracer for PM2.5 from biomass burning emissions (Fig. 5). Past

384

studies pointed out that personal EC exposure was associated with individuals’ activity patterns

385

(Huang et al., 2015; Tunno et al., 2016). For example, Dons et al. (2011) indicated that transport

386

microenvironment was one of the most important contributors to personal EC exposure. They

387

pointed out that 6% of the time in transport contributed over 20% of total personal

388

exposure. Subject-specific activity patterns are listed in Tables S8‒S9 by season. In our recent

389

publication, we found that for a one-hour extra time in transit an average increase of 0.47 µg/m3

390

(95% CI: 0.36–0.67 µg/m3) in personal EC exposure was shown (Chen et al., 2018).

391

The average Fe/Ca (1.89) and Mn/Ca (0.08) ratios in personal PM2.5 were comparable with the

392

corresponding values in soil and paved road dust samples (0.8–2.6 and 0.01–0.08, respectively)

393

in Hong Kong (Ho et al., 2003a; Ho et al., 2003b). Average personal exposure to OC, Ca, Ti, Fe,

394

Se, Rb, Sr, and Zr were significantly higher than those in ambient PM2.5, with

395

personal_avg/ambient_avg ratios greater than unity in both seasons (Fig. 2). Further, personal

17

396

exposure to crustal/dust particles (Ca2+, Mg, Si, Ca, Ti) and non-tailpipe traffic-related particles

397

(e.g., Mn, Fe) did not exhibit apparent seasonal trends. These sources contributed to personal

398

PM2.5 due to exposure when subjects stayed indoors, near roadways, and in transit (e.g., bus,

399

metro). These results were consistent with previous findings (Dons et al., 2011; Spira-Cohen et

400

al., 2010), which indicated that indoor exposure to ambient elements (e.g., Mn, Fe, Sr, Mo)

401

significantly decreases with road proximity (Huang et al., 2018). Additionally, Chen et al. (2018)

402

reported increment (per hour) of time in transit associated with increased personal exposure to Ti

403

and Fe (p < 0.05) in adults in Hong Kong.

404

In this investigation, robust correlations were shown (rs: 0.66–0.79; p < 0.01) between

405

crustal/road dust and non-tailpipe pollutants (Fig. 5). Personal exposure to crustal/road dust

406

particles and non-tailpipe pollutants varied across subject groups. Personal EC, Ca2+, and Sr

407

exposures were significantly higher (p < 0.05) for non-office workers than other subjects. The

408

findings consistent with previous studies, for example, Huang et al. (2014) reported significantly

409

higher crustal particles (Si, Al, Ca, Ti) among truck drivers than those of office workers in

410

Beijing. It should be noted that the highest ambient levels (43.2 µg/m3, p = 0.02) coincided with

411

the significantly higher PM2.5 component exposure (p < 0.05) for housewives compared to other

412

subjects (Table 2). No direct correlation was observed between the analyzed PM2.5 components

413

exposure levels with any of our recorded indoor activities in the present study.

414

Agrawal et al. (2009) reported V/Ni ratio > 2.2 in heavy fuel oil. Wang et al. (2018) also

415

reported an average V/Ni ratio of 3.1 in the Port of Shanghai. In this study, V/Ni ratio in ambient

416

PM2.5 varied from 2.9–3.3 with an average of 3.2 across the seven ambient sites. A relatively

417

higher V/Ni ratio of 4.5 was shown in personal PM2.5 throughout the sampling period. Similar

418

results were obtained in ambient PM2.5 in Spain with V/Ni ratio of 4 ± 1 (Viana et al., 2009). The

18

419

results above agreed with the previous findings (Viana et al., 2014). A strong correlation

420

between Ni and V (rs = 0.86; p < 0.01) along with personal_avg/ambient_avg ratios < 1 for both

421

species, indicating the contribution of shipping emissions to personal PM2.5 exposure.

422

PMF identified six sources, including regional pollution, secondary sulfate, secondary nitrate,

423

tailpipe exhaust, crustal/road dust, and shipping emissions for both ambient and personal datasets.

424

The sources represented by regional pollution, secondary sulfate, and secondary nitrate

425

contributed 55.7% of ambient PM2.5, which was considerably lower than that in personal PM2.5

426

exposures (62.9%). In a previous study conducted by (Huang et al., 2013), whose findings are

427

consistent with the current research, they revealed that secondary sulfate (31%), regional

428

pollution (mainly biomass burning) (23%), and secondary nitrate (13%) were three dominant

429

contributing sources to ambient PM2.5 at the suburban AQMS in Hong Kong. Lu and Fung (2016)

430

pointed out that superregional transport (e.g., power plant, industry emissions) over Hong Kong

431

was an essential contributor to sulfates (73‒92%) and nitrates (30‒76%) both in the summer and

432

winter seasons Chen et al. (2018) indicated that time spent indoors (e.g., at home) associated

433

with personal exposure to SIA. In the current study, significant higher personal SIA exposures

434

were found in housewives compared with other subjects because they spent more time indoors

435

(86.0‒90.5%) than their counterparts (47.9‒74.2%).

436

The sources represented by tailpipe exhaust and crustal/road dust contributed 18.2% and 9.4%

437

of ambient PM2.5, which were also lower than that for personal PM2.5 exposure (19.0% and 9.9%,

438

respectively). For personal PM2.5 exposure, dust-related pollution was separated into two parts

439

(i.e., 5.2 % of crustal/road dust, 4.7% of the non-tailpipe traffic-related fraction), reflecting the

440

influence of personal activity (e.g., in transit) on total exposure levels. The latter represent

19

441

personal exposure to particles of non-ambient origin (e.g., indoor-generated particles, personal

442

activity events while indoors and outdoors).

443

Although there were discrepancies in the number of factors (5 or 6 vs. 7), the most identifiable

444

PMF factors agreed well with the identified PCs. Furthermore, most of the PMF factors were

445

easily distinguishable from others by notable differences in the source profiles. For example, the

446

PMF model can distinguish between the non-tailpipe traffic-related particles and crustal/road

447

dust, separate secondary sulfate and secondary nitrate, and regional pollution. Our results are

448

consistent with the findings in (Cesari et al., 2016; Ito et al., 2004), where an inter-comparison of

449

source apportionment in ambient PM2.5 and PM10 was conducted using both the PCA and PMF

450

models.

451

PCA/APCs‒LMM analysis revealed that a mixed source (regional pollutants, secondary

452

aerosols, and tailpipe exhaust) (65.4%), shipping emission (9.3%), crustal/road dust (9.3%), and

453

non-tailpipe traffic-related particles (4.3%) were positive and significant contributors of personal

454

PM2.5 exposure. This agreed with previous findings in Guangzhou (Chen et al., 2017b), which

455

reported that regional air pollution (50.4 ± 0.9%), tailpipe exhaust (8.6 ± 0.7%), crustal/road dust

456

(5.8 ± 0.7%), and biomass burning emissions (2.0 ± 0.2%) were positive sources of personal

457

PM2.5 exposure in Guangzhou.

458

We also applied PCA/APCs‒LMM analysis to characterize the seasonal and subject group

459

variations of source contributions to personal PM2.5 exposure. Backward elimination resulted in a

460

multi-sources regression model (Tables 4‒5). The results showed that fresh sea-salt (Cl-) was not

461

a significant contributor to personal PM2.5 exposure compared with other sources in the summer.

462

A portion of regional pollution (mainly industrial activities, with high loadings of Mn and Pb)

463

contributes 5.5% of personal PM2.5 in winter. Moreover, secondary aerosols mixed with regional

20

464

pollutant contributed 35.1‒43.6% with no seasonal or subject group differences (p > 0.05).

465

Previous studies suggested that variations of regional pollutants were mostly due to the influence

466

of temporal variability in the ambient environment (Minguillon et al., 2012).

467

In this investigation, the source contribution of tailpipe exhaust to personal PM2.5 exposure

468

was exceptionally high, reaching 50% in summer (p < 0.001). Huang et al. (2013) indicated that

469

vehicle emissions were more dominant during sampling periods with lower PM2.5 concentrations

470

in Hong Kong. In addition, the contribution of tailpipe exhaust to personal PM2.5 exposure varied

471

widely across subject groups (33.3% vs. 40.5%; p < 0.05). This may be attributed to the variation

472

of time in transit (Chen et al., 2018). For example, non-office workers spent about 4.2‒4.6 hours

473

(i.e., 17.3‒19.3%) of daily time in a vehicle, which was significantly higher than their

474

counterparts (range of 1.4‒4.4%, p < 0.001) (Tables S8-S9). Shang et al. (2019) reported that

475

higher roadway transport emissions were found in guards than in students in Beijing, which was

476

related to guards spending a higher proportion of time outdoors. Higher non-tailpipe traffic

477

contributions (8.2%) to personal PM2.5 exposure was also found in group B when compared to

478

group A. Contributions of traffic-related particles to personal PM2.5 exposure were significantly

479

higher (p < 0.001) in summer (6.7%) than in winter (3.0%), which can be explained by the

480

greater amount of time in transit in summer (5.9 ± 10.0%) than in winter (4.0 ± 7.8%) for all

481

participants in the current study.

482

Previous studies targeted the contribution of shipping emissions to ambient pollution without

483

considering individual or population exposure. For example, Tian et al. (2013) linked elevated

484

ambient Ni and V concentrations with increased cardiovascular hospitalizations in Hong Kong.

485

Pun et al. (2014) pointed out that residual oil combustion (characterized by Ni and V)

486

contributed 8.0% of ambient PM2.5 in Hong Kong. Lin et al. (2018) indicated that each IQR

21

487

increase (median = 1.0 µg/m3) in lag1 shipping emission was associated with a 5.6% (95% CI:

488

0.8-10.5%) increment in cardiovascular mortality in Guangzhou, China. This investigation

489

highlights the importance of shipping emission contributions to personal PM2.5 exposures.

490

Concurrent ambient PM2.5 species were not available. Thus, seasonal average ambient data

491

during the same sampling period were used for comparison purpose. In this analysis, we reported

492

the seasonal personal_avg/ambient_avg ratio instead of daily personal/ambient ratios for PM2.5

493

components. There were some limitations: 1) although repeated personal monitoring was

494

conducted, exposure error cannot be eliminated as a possibility; 2) analytical uncertainties for

495

each species might also influence the results; 3) the small number of subjects may limit the

496

prediction power. Even with these limitations, the model structure and the use of source profile

497

constraints provide a useful tool for evaluating various source contributions to personal PM2.5

498

exposure. These two approaches (PMF vs. PCA/APCs-LMM) are complementary and allowed

499

for the evaluation of the dataset, both collectively and separately. Further investigation of the

500

complex relationships between long-term source-specific pollution exposure and health effects

501

are needed for better air pollution control policies and evidence-based public health

502

interventions.

503 504

5. Conclusions

505

Determining source contributions of individual or population exposure remains a challenge in

506

epidemiological studies. We applied source apportionment technique, including PMF and

507

PCA/APCs-LMM analyses to investigate and to explain the variability in source contributions to

508

personal PM2.5 exposure in adult subjects in Hong Kong. We identified seven source profiles for

509

personal PM2.5 exposure using PMF. This study revealed that the combination of PCA/APCs

22

510

with LMM analysis could be used to determine source contributions to personal PM2.5 exposure,

511

as well as to assess the seasonal and occupational variations in source contributions. We found

512

that regional pollution (including secondary inorganic aerosols) and tailpipe exhaust are

513

dominant sources contributing to personal PM2.5 exposures. Our findings also highlighted the

514

significance of shipping emissions to individual PM2.5 exposures for Hong Kong residents. The

515

most crucial difference between personal and ambient modeling results was that non-tailpipe

516

traffic emission plays a unique role in influencing personal PM2.5 exposure (3.0‒7.4%, p <

517

0.001). Different source apportionment approaches for investigating source contributions to total

518

personal exposure can be further applied to source-specific health risks assessment.

519 520

Author Contributions

521

XCC was involved in data analysis and manuscript preparation. TW and KFH designed the

522

research. XCC, JJC, and SCL performed sample collection and laboratory work. TW, HLY, and

523

KFH revised the manuscript. HLY and NCL supervised the development of study and

524

manuscript evaluation. All authors read and approved the manuscript.

525 526

Conflict of interest

527

The authors declare that they have no competing interests.

528 529

Acknowledegments

530

This study was supported by the Hong Kong Environmental Protection Department (contract

531

No.13-04909); a grant from the Research Grants Council of the Hong Kong Special

532

Administrative Region of China (grant number 412413); and the Ministry of Science and 23

533

Technology of China (grant number 2013FY112700). Xiao-Cui Chen acknowledges the Vice-

534

Chancellor’s Discretionary Fund of The Chinese University of Hong Kong (grant number

535

4930744). The authors would like to thank Chi-Sing Chan for assistance in the laboratory. Dr.

536

Ming Luo and Ms. Ho-Yee Poon provided valuable technical guidance.

537 538

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31

1 2

Tables Table 1. Summary of personal exposure to PM2.5 constituents during summer and winter for adult subjects of Hong Kong. Summer (n = 80*) Winter (n = 80) Mean (SDa) Median > MDLs Mean (SDa) Median > MDLs Personal PM2.5 exposure (µg/m3) 30.2 (16.9) 28.1 100.0% 39.8 (19.1) 36.1 100.0% Carbonaceous aerosols OC (µg/m3) 7.1 (3.3) 6.5 100.0% 8.0 (4.6) 7.0 100.0% EC (µg/m3) 2.1 (1.1) 2.1 100.0% 2.3 (1.0) 2.1 100.0% Water-soluble ions Na+ (µg/m3) 0.5 (0.3) 0.5 95.0% 0.6 (0.3) 0.5 97.5% NH4+ (µg/m3) 3.1 (2.4) 2.8 95.0% 4.6 100.0% 5.0 (2.6) K+ (µg/m3) 0.2 (0.2) 0.3 95.0% 0.3 100.0% 0.4 (0.2) Mg2+ (µg/m3) 0.03 (0.03) 0.0 60.0% 0.1 100.0% 0.08 (0.04) Ca2+ (µg/m3) 0.3 (0.6) 0.2 98.8% 0.2 (0.1) 0.2 96.3% Cl- (µg/m3) 0.2 (0.2) 0.1 98.7% 0.4 97.5% 0.6 (0.7) NO3- (µg/m3) 1.1 (1.2) 0.7 100.0% 3.3 100.0% 4.9 (4.7) SO42- (µg/m3) 8.2 (5.7) 7.2 100.0% 10.5 100.0% 11.3 (5.6) oxalate (µg/m3) 0.2 (0.2) 0.2 76.3% 0.3 97.5% 0.4 (0.2) Elements Na (ng/m3) 3000 (2281) 2731 93.8% 98.8% 3718 (2142) 3518 3 Mg (ng/m ) 94.4 (102.2) 59.8 65.0% 109.9 (111.4) 82.1 68.8% Al (ng/m3) 102.8 (101.1) 107.4 92.5% 91.3% 144.8 (103.8) 134.1 Si (ng/m3) 209.4 (310.9) 150.0 95.0% 249.0 (197.1) 220.8 98.8% S (ng/m3) 2459 (1722) 2528 100.0% 100.0% 3041 (1305) 3000 3 Cl (ng/m ) 53.8 (92.6) 36.8 94.9% 98.8% 282.2 (322.4) 158.4 K (ng/m3) 243.3 (181.6) 235.3 100.0% 100.0% 407.1 (258.8) 363.1 Ca (ng/m3) 257.8 (768.2) 128.3 98.8% 169.3 (115.6) 145.6 100.0% Ti (ng/m3) 12.5 (12.1) 10.4 97.5% 14.4 (12.7) 13.0 96.3% V (ng/m3) 14.6 (14.7) 7.5 98.8% 12.9 (16.2) 7.8 95.0% Cr (ng/m3) 1.5 (2.0) 0.5 32.5% 73.8% 2.4 (2.1) 1.8 Mn (ng/m3) 11.1 (8.7) 9.7 88.8% 13.6 (8.2) 13.4 98.8% Fe (ng/m3) 307.5 (320.5) 185.9 100.0% 267.6 (201.2) 222.5 100.0% 3 Ni (ng/m ) 3.7 (4.1) 2.3 81.3% 4.3 (4.3) 3.2 96.3% 3 Cu (ng/m ) 16.5 (16.0) 13.5 91.3% 100.0% 27.6 (26.8) 19.7 Zn (ng/m3) 114.7 (137.5) 98.7 100.0% 124.8 (96.6) 111.4 100.0% As (ng/m3) 1.6 (1.3) 1.2 58.8% 68.8% 2.6 (2.4) 2.0 3 Se (ng/m ) 0.7 (0.8) 0.3 31.3% 1.1 (1.0) 0.8 62.5% Br (ng/m3) 8.7 (6.2) 8.3 95.0% 100.0% 19.4 (13.9) 15.4 Rb (ng/m3) 1.0 (1.0) 0.7 53.8% 1.5 (1.3) 1.3 73.8% Sr (ng/m3) 1.4 (2.1) 1.1 73.8% 93.8% 2.3 (1.7) 1.9 3 Zr (ng/m ) 1.2 (1.0) 1.1 57.5% 1.2 (1.0) 0.8 51.3% 3 Mo (ng/m ) 2.0 (2.8) 0.7 42.5% 1.2 (1.2) 0.7 31.3% Sn (ng/m3) 8.8 (13.1) 4.1 37.5% 11.2 (8.8) 9.3 56.3% Pb (ng/m3) 19.0 (19.2) 12.7 71.3% 100.0% 31.2 (24.7) 27.3 Total elements (µg/m3) 7.0 (4.8) 6.9 / 8.7 (4.0) 8.4 / 28.0 (1.7) 28.3 / 17.1 (2.4) 16.8 / Meteorological conditions Temperature (℃) Relative humidity (%) 74.9 (8.2) 76.8 / 74.1 (11.9) 75.6 / Rain fall (mm) 5.1 (13.7) 0.2 / 1.3 (3.9) 0.1 / a Notes: SD denotes standard deviation. PM2.5 component concentrations below the method detection limit (MDL) were replaced by half of the MDL values. A paired t-test was used to determine p-values for the difference between seasons. Bolded p-values indicated significant differences at the 0.05 level. *By Dixon’s test (α = 0.05), one extreme outlier was identified and removed from the dataset.

p-value

Component

3 4 5 6

0.001 0.13 0.35 0.61 < 0.001 0.001 < 0.001 0.31 < 0.001 < 0.001 0.001 < 0.001 0.04 0.36 0.01 0.34 0.02 < 0.001 < 0.001 0.31 0.34 0.50 0.008 0.07 0.35 0.34 0.002 0.59 0.001 0.004 < 0.001 0.002 0.004 0.63 0.02 0.17 0.001 0.02 < 0.001 0.70 0.05

7 8 9

10 11 12

Table 2. Level of personal exposure to PM2.5 mass and components for office workers, students, housewives, and non-office workers of Hong Kong on the examination days. Office workers Students Housewives Non-office workers (observations = 38) (observations = 59) (observations = 38) (observations = 25*) Component Mean (SDa) > MDLs Mean (SDa) > MDLs Mean (SDa) > MDLs Mean (SDa) > MDLs 3 Personal PM2.5 exposure (µg/m ) 27.2 (14.6) 100.0% 33.0 (18.1) 100.0% 100.0% 37.5 (22.1) 100.0% 44.1 (17.0) Ambient PM2.5 (µg/m3) 28.6 (16.5) 100.0% 34.7 (21.2) 100.0% 100.0% 34.5 (20.5) 100.0% 43.2 (16.5) P/A ratio 1.1 (0.5) 100.0% 1.1 (0.5) 100.0% 1.1 (0.5) 100.0% 1.4 (1.4) 100.0% OC (µg/m3) 5.7 (2.6) 100.0% 7.6 (4.0) 100.0% 100.0% 8.3 (4.7) 100.0% 8.9 (4.4) 3 EC (µg/m ) 1.7 (0.9) 100.0% 1.9 (0.8) 100.0% 2.5 (1.0) 100.0% 100.0% 3.0 (1.2) Na+ (µg/m3) 0.5 (0.3) 89.5% 0.6 (0.4) 96.6% 0.6 (0.2) 100.0% 0.5 (0.2) 100.0% NH4+ (µg/m3) 3.1 (2.3) 100.0% 3.8 (2.4) 96.6% 100.0% 3.3 (2.9) 92.0% 5.9 (2.6) + 3 K (µg/m ) 0.2 (0.1) 97.4% 0.3 (0.2) 96.6% 100.0% 0.3 (0.2) 96.0% 0.4 (0.2) Mg2+ (µg/m3) 0.05 (0.04) 71.1% 0.05 (0.04) 78.0% 0.07 (0.04) 92.1% 0.08 (0.15) 80.8% Ca2+ (µg/m3) 0.2 (0.2) 97.4% 0.2 (0.1) 94.9% 0.3 (0.1) 100.0% 100.0% 0.5 (1.0) Cl- (µg/m3) 0.4 (0.4) 97.4% 0.5 (0.7) 98.3% 0.4 (0.5) 100.0% 0.3 (0.3) 96.0% 3 NO3 (µg/m ) 2.4 (2.5) 100.0% 2.9 (4.0) 100.0% 100.0% 2.1 (3.2) 100.0% 4.5 (5.0) SO42- (µg/m3) 7.0 (4.5) 100.0% 9.5 (5.5) 100.0% 100.0% 8.5 (6.1) 100.0% 13.7 (5.2) oxalate (µg/m3) 0.2 (0.2) 73.7% 0.3 (0.2) 89.8% 92.1% 0.2 (0.2) 92.0% 0.4 (0.3) 2218 (1821) 89.5% 3135 (2138) 96.6% 100.0% 3231 (2231) 100.0% Na (ng/m3) 4931 (1948) 3 Mg (ng/m ) 75.7 (90.9) 52.6% 102.5 (115.7) 64.4% 81.6% 73.6 (83.8) 72.0% 146.9 (109.2) Al (ng/m3) 92.0 (100.2) 86.8% 110.2 (88.3) 89.8% 97.4% 136.5 (128.1) 96.0% 168.3 (101.8) 194.6 (225.6) 92.1% 193.1 (163.4) 96.6% 280.4 (225.3) 100.0% 289.0 (463.1) 100.0% Si (ng/m3) 3 S (ng/m ) 2051 (1384) 100.0% 2601 (1434) 100.0% 100.0% 2514 (1765) 100.0% 3837 (1178) Cl (ng/m3) 140.7 (162.2) 97.4% 180.2 (309.8) 96.6% 188.6 (274.7) 94.7% 154.7 (263.3) 100.0% K (ng/m3) 217.9 (161.1) 100.0% 345.3 (284.7) 100.0% 100.0% 349.8 (244.4) 100.0% 385.1 (184.5) Ca (ng/m3) 160.3 (181.1) 100.0% 154.4 (133.4) 98.3% 173.0 (100.3) 100.0% 496.0 (1339) 100.0% 3 Ti (ng/m ) 13.3 (18.6) 92.1% 11.2 (6.7) 98.3% 14.5 (7.9) 97.4% 17.2 (15.9) 100.0% V (ng/m3) 14.0 (16.1) 97.4% 11.5 (11.8) 94.9% 97.4% 10.0 (10.9) 100.0% 19.5 (20.5) Cr (ng/m3) 1.9 (2.4) 44.7% 2.2 (2.2) 59.3% 1.6 (1.5) 52.6% 1.9 (2.1) 52.0% Mn (ng/m3) 11.2 (8.4) 94.7% 11.2 (7.4) 88.1% 13.4 (6.7) 97.4% 15.0 (12.3) 100.0% 257.9 (253.6) 100.0% 291.8 (296.2) 100.0% 247.9 (215.5) 100.0% 382.7 (279.2) 100.0% Fe (ng/m3) Ni (ng/m3) 4.0 (4.3) 86.8% 3.4 (3.1) 89.8% 92.1% 3.0 (3.4) 84.0% 5.6 (5.5) Cu (ng/m3) 19.7 (17.5) 94.7% 23.3 (25.6) 91.5% 25.9 (27.5) 100.0% 17.1 (11.0) 100.0% 3 Zn (ng/m ) 121.3 (140.4) 100.0% 114.1 (111.2) 100.0% 115.6 (64.0) 100.0% 137.2 (161.5) 100.0% As (ng/m3) 1.3 (1.3) 44.7% 2.5 (2.3) 66.1% 78.9% 1.8 (1.9) 64.0% 2.6 (1.8) Se (ng/m3) 0.7(0.9) 28.9% 1.0 (0.9) 57.6% 1.2 (1.1) 60.5% 0.6 (0.8) 28.0% 3 Br (ng/m ) 10.9 (9.5) 97.4% 15.8 (14.1) 96.6% 16.5 (11.1) 97.4% 11.3 (10.2) 100.0% Rb (ng/m3) 0.8 (0.8) 39.5% 1.3 (1.3) 66.1% 1.6 (1.0) 81.6% 1.2 (1.3) 68.0% Sr (ng/m3) 1.4 (1.2) 71.1% 1.7 (1.1) 79.7% 1.9 (1.1) 94.7% 96.0% 2.9 (4.1) Zr (ng/m3) 1.1 (0.9) 52.6% 1.1 (0.8) 52.5% 1.4 (1.1) 60.5% 1.3 (1.2) 52.0% 3 Mo (ng/m ) 1.8 (2.3) 31.6% 1.6 (2.5) 37.3% 1.4 (1.8) 39.5% 1.7 (1.7) 40.0% 3 Sn (ng/m ) 7.4 (7.2) 28.9% 11.7 (15.3) 54.2% 11.3 (9.0) 57.9% 8.0 (5.6) 40.0% Pb (ng/m3) 18.0 (20.7) 76.3% 27.4 (23.9) 83.1% 31.9 (22.2) 97.4% 20.3 (21.5) 88.0% Total elements (µg/m3) 5.6 (3.9) / 7.4 (4.2) / / 8.7 (6.8) / 10.6 (3.4) a Notes: SD denotes standard deviation. Mean exposure differences across different groups of subjects. Bolded p-values indicated significant mass differences at the 0.05 level. *By Dixon’s test (α = 0.05), one extreme outlier was identified and removed from the dataset.

p-value < 0.001 0.02 0.45 0.004 < 0.001 0.14 < 0.001 0.004 0.31 0.02 0.36 0.04 < 0.001 < 0.001 < 0.001 0.011 0.007 0.21 < 0.001 0.65 0.01 0.06 0.21 0.03 0.62 0.19 0.27 0.02 0.58 0.78 0.008 0.03 0.08 0.01 0.02 0.51 0.92 0.27 0.07 0.001

13 14 15

16 17 18 19 20 21 22 23 24 25 26 27 28 29

Table 3. Principal component analysis performed on personal PM2.5 components, resulting in five independent factors. Varimax rotated principal component loading matrix. Species PC 1c PC 2 PC 3 PC 4 PC 5 Communalities *a * * * OC 0.52 0.40 * * * * EC 0.63 0.62 * * * * Ammonium 0.88 0.91 * * * * Potassium 0.85 0.83 * * * * Magnesium 0.64 0.67 * * * * Chloride 0.84 0.75 * * * Nitrate 0.62 0.58 0.75 * * * * Sulfate 0.85 0.85 * * * * Oxalate 0.75 0.72 * * * * Na 0.83 0.76 * * * Al 0.69 0.45 0.78 * * * * Si 0.94 0.92 * * * * Ca 0.95 0.94 * * * * Ti 0.86 0.83 * * * * V 0.94 0.92 * * * * Cr 0.91 0.90 * * * * Mn 0.87 0.89 * * * Fe 0.68 0.60 0.87 * * * * 0.92 0.91 Ni * * * * Cu 0.40 0.43 * * * * Zn 0.89 0.81 * * * * As 0.80 0.82 * * * * 0.75 0.78 Br * * * Rb 0.70 0.60 0.85 * * * * Sr 0.92 0.89 * * * * 0.68 Zr 0.74 * * * * Pb 0.82 0.89 Eigenvalue 7.60 7.58 2.45 2.16 1.56 % of Varianceb 28.15 28.07 9.09 8.01 5.79 Potential sourced Factor 3 Factor 2+5+7+6 Factor 1 Factor 2_1e Factor 4 Notes: a *Factor loadings between -0.40 and 0.40 not shown. b % of variance: percentage of variance explained by each factor. c PC denotes principal component. d Major sources identified using PCA as matched with corresponding PMF factor for comparison. e Factor 2-1: secondary nitrate.

30 31 32 33

34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58

Table 4. The results of the regression analysis for fixed effects related to personal PM2.5 exposures (n = 160). Contribution from major sources identified using PCA/APCs-LMM (as matched with corresponding PMF factors for comparison). R2c (R2m) Estimate/(Contribution) SEa p-valueb Intercept 4.4 1.4 0.001 0.87 APCs-1 (Factor 3) 7.8 (9.3%) 0.6 < 0.001 (0.56) APCs-2 (Factor 2+5+7+6) 14.6 (61.7%) 0.6 < 0.001 (0.80) APCs-3 (Factor 1) 3.8 (9.3%) 0.5 < 0.001 (0.22) APCs-4 (Factor 2) 6.0 (3.7%) 0.6 < 0.001 (0.33) APCs-5 (Factor 4) 1.9 (4.3%) 0.5 < 0.001 (0.06) Notes: Results are from a mixed-effects model that included a random part for subjects. Columns in the table show independent predictors for personal PM2.5, regression coefficient (Estimate) and its standard error (SEa), the significance of the total model (p-value) and the coefficient of the determination (R2). The marginal R2 statistic for the overall mixed-effects model is marked in bold. b The level of significance was taken as p < 0.05.

59 60 61

Table 5. The results of the regression analysis for fixed effects related to personal PM2.5 exposures by season and by subject group. Contribution from major sources identified using PCA/APCs-LMM (as matched with corresponding PMF factors).

Intercept APCs_s1 (Factor 3) APCs_s2 (Factor 2+5+7) APCs_s3 (Factor 1) APCs_s4 (Factor 6) APCs_s5 (Factor 4) APCs_s6 (Factor 8_2)d Group A (nf = 97) Intercept APCs_a1 (Factor 6+7) APCs_a2 (Factor 2+5) APCs_a3 (Factor 3) APCs_a4 (Factor 4) APCs_a5 (Factor 1)

62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

Summer (nf = 80) Estimate (Contribution) SEa p-valueb -2.4 1.31 0.08 10.4 (3.5%) 0.54 < 0.001 10.6 (43.6%) 0.52 < 0.001 0.50 < 0.001 3.0 (7.0%) 0.53 < 0.001 9.9 (52.6%) 0.50 < 0.001 2.7 (6.7%) -0.1 0.48 0.84 Estimate (Contribution) 3.3 10.5 (33.3%) 10.9 (39.6%) 3.5 (0.4%) 3.0 (6.4%) 3.5 (8.4%)

SEa 1.08 0.52 0.51 0.48 0.48 0.47

p-valueb 0.003 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

R2c (R2m) 0.93 (0.80) (0.80) (0.17) (0.78) (0.13) R2c (R2m) 0.92 (0.83) (0.84) (0.34) (0.27) (0.34)

Intercept APCs_w1 (Factor 2+5) APCs_w2 (Factor 6+7) APCs_w3 (Factor 3) APCs_w4 (Factor 7_1)e APCs_w5 (Factor 4) APCs_w6 (Factor 1) Group B (nf = 63) Intercept APCs_b1 (Factor 3+4) APCs_b2 (Factor 2+5+7) APCs_b3 (Factor 1) APCs_b4 (Factor 2_1)c APCs_b5 (Factor 6)

Winter (nf = 80) Estimate (Contribution) SEa p-valueb 7.7 1.52 < 0.001 14.5 (37.4%) 0.72 < 0.001 0.70 < 0.001 9.2 (21.1%) 3.8 (3.2%) 0.67 < 0.001 2.6 (2.9%) 0.66 0.0001 0.68 0.01 1.8 (3.0%) 0.67 < 0.001 3.8 (8.3%) Estimate (Contribution) 3.7 9.0 (7.4%) 11.3 (35.1%) 2.7 (6.2%) 6.7 (2.7%) 10.9 (40.5%)

SEa 2.27 1.94 0.93 0.94 0.91 0.92

p-valueb 0.11 < 0.001 < 0.001 0.007 < 0.001 < 0.001

R2c (R2m) 0.91 (0.86) (0.71) (0.29) (0.17) (0.08) (0.29) R2c (R2m) 0.87 (0.27) (0.72) (0.12) (0.48) (0.71)

Notes: Results are from a mixed-effects model that included a random part for subjects. Columns in the table show independent predictors for personal PM2.5, regression coefficient (Estimate) and its standard error (SE)a, the significance of the total model (p-value) and the coefficient of the determination (R2). The conditional R2 statistic for the overall mixed-effects model is marked in bold. b The level of significance was taken as p < 0.05. c Factor 2_1: Secondary nitrate. d Factor 8_2: Fresh sea-salt (Cl-). e Factor 7_1: Regional pollution (industrial activities). f n, number of samples.

Figure Captions Figure 1. Map of the study area in Hong Kong with subjects’ residential location, the location of air quality monitoring stations, meteorological stations, power stations, container terminals, and airport. Note: Pollution wind rose (upper side) during the sampling campaign is also provided. Figure 2. Distribution (maximum, 75th percentile, the median, 25th percentile, and the minimum) of personal_avg/ambient_avg ratios in summer and winter. Figure 3. The source profiles illustrated as percentages of PM2.5 species (%) by positive matrix factorization model in personal exposure. Figure 4. Contributions of identified sources to personal PM2.5 exposure. Figure 5. Spearman’s correlation coefficients (rs) matrix for personal PM2.5 exposure and its components. Figure 6. Source contribution analysis for personal PM2.5 exposure using PCA/APCsLMM.

Fig. 1. Map of the study area in Hong Kong with subjects’ residential location, the location of air quality monitoring stations, meteorological stations, power stations, container terminals, and airport. Note: Pollution wind rose (upper side) during the sampling campaign is also provided.

Fig. 2. Distribution (maximum, 75th percentile, the median, 25th percentile, and the minimum) of personal_avg/ambient_avg ratios in summer and winter.

Fig. 3. The source profiles illustrated as percentages of PM2.5 species (%) by positive matrix factorization model in personal exposure.

Fig. 4. Contributions of identified sources to personal PM2.5 exposure.

Fig. 5. Spearman’s correlation coefficients (rs) matrix for personal PM2.5 exposure and its components.

Fig. 6. Source contribution analysis for personal PM2.5 exposure using PCA/APCs-LMM.

Highlights: •

Personal exposure to PM2.5 mass and chemical components often exceeds the corresponding ambient measurements.

•

PMF analysis identified seven PM2.5 source factors from samples collected during personal monitoring of adults in Hong Kong.

•

PCA/APCs combined with linear mixed-effects models were applied to account for discrepancies in seasonal and occupational source contributions.

•

Daily individual activities influenced PM2.5 exposures from traffic-related sources.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: